Explore Dartmouth Data

# Install packages if needed
package_list <- c("tidyverse", "stringr", "foreign", "ggmap", "tigris", "raster", "maptools", "viridis", "rmarkdown", "knitr")
new_packages <- package_list[! package_list %in% installed.packages()[,"Package"]]
if(length(new_packages)) install.packages(new_packages)
library(stringr) # for string manipulation
# library(janitor) # for handy tools to inspect/clean data
library(foreign) # For reading in different file formats (incl DBF)
library(ggmap) # for additional ggplot2 themes (eg. theme_map)
library(tigris) # for downloading census shapefiles
# library(rgeos) # for handling spatial data
library(raster) # for erase function to clip tracts to shoreline (erase water)
library(maptools) # for manipulating spatial data
library(viridis) # for color palettes
library(tidyverse) # for tidy data manipulation

dart_raw <- read.dbf("../dropbox/capstone/data/raw/t_103113_1.dbf", as.is = TRUE)
names(dart_raw) <- names(dart_raw) %>% str_to_lower()
xwalk <- 
  read_csv("../dropbox/capstone/data/crosswalks/gent_xwalk.csv", col_types = cols(geoid = "c")) %>% 
  mutate(gent = recode(gent_ind, `0` = NA_character_, `1` = "Gentrifying", 
                                 `2` = "Non-Gentrifying", `3` = "Higher-Income"),
         gent = ordered(gent, levels = c("Gentrifying", "Non-Gentrifying", "Higher-Income"))) %>% 
  select(geoid, gent) %>% 
  filter(!is.na(gent))

dart_nyc <- 
  dart_raw %>% 
  as_data_frame() %>% 
  filter(county %in% c("36005", "36047", "36061", "36081", "36085")) %>% 
  transmute(geoid = str_c(county, tract),
            boro = recode(county, "36005" = "Bronx", "36047" = "Brooklyn", "36061" = "Manhattan", 
                                  "36081" = "Queens", "36085" = "Staten Island"),
            medicare_denom = tbene_n10,
            medicare_acscd = tacs_10) %>% 
  mutate_if(is.double, funs(if_else(. %in% c(-99, -999), NA_real_, .))) %>% 
  mutate(acscd_rt = if_else(medicare_denom != 0, (medicare_acscd / medicare_denom)*1000, NA_real_)) %>% 
  inner_join(xwalk, by = "geoid")
head(dart_nyc)

dart_nyc %>% 
  filter(!is.na(acscd_rt)) %>%
  group_by(gent) %>% 
  summarise(acscd_rt = sum(medicare_acscd) / sum(medicare_denom) * 1000) %>% 
  ggplot(aes(gent, acscd_rt, fill = gent)) +
  geom_col() +
  geom_text(aes(y = acscd_rt + 5, label = round(acscd_rt, 1))) +
  scale_fill_viridis(discrete = TRUE) +
  labs(title = "Ambulatory Care Sensitive Condition Discharges",
       subtitle = "per 1000 Medicare Benificiaries",
       x = "", y = "", fill = "")

boros <- c("005", "047", "0061", "081", "085")

Warning message:
In scan(file = file, what = what, sep = sep, quote = quote, dec = dec,  :
  EOF within quoted string

p <-
  tracts_df %>% 
  ggplot(aes(x= long, y = lat, group = group, fill = acscd_rt)) +
  geom_polygon() +
  geom_polygon(fill = NA, color = "white", size = 0.10) +
  coord_map() +
  scale_fill_viridis(option = "magma") +
  theme_nothing(legend = TRUE) +
  labs(title = "Ambulatory Care Sensitive Condition Discharges \nper 1,000 Medicare Beneficiaries",
       subtitle = "New York City, 2010",
       fil = "",
       caption = "Source: Dartmouth Atlas of Health Care") +
  theme(legend.title = element_blank(),
        legend.position = c(.1, .7),
        plot.caption = element_text(colour = "grey50"))
ggsave("acsc_map.png", p, width = 10, height = 10, units = "in")

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p <-
  tracts_df %>% 
  ggplot(aes(x= long, y = lat, group = group, fill = gent)) +
  geom_polygon() +
  geom_polygon(fill = NA, color = "white", size = 0.10) +
  coord_map() +
  scale_fill_viridis(discrete = TRUE) +
  theme_nothing(legend = TRUE) +
  labs(title = "Gentrification Status by Tract",
       subtitle = "New York City, 2010",
       fil = "",
       caption = "Source: Dartmouth Atlas of Health Care") +
  theme(legend.title = element_blank(),
        legend.position = c(.1, .7),
        plot.caption = element_text(colour = "grey50"))
ggsave("gent_map.png", p, width = 10, height = 10, units = "in")

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